Diffusion for Text: Why Mercury Could Make LLMs 10x Faster
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What is the main topic discussed in this episode?
We were matching the perplexity, but we were able to be like 10 times faster. That was super exciting to me. And I really wanted to see what happens if you train something bigger than a GPT-2 model, possible to build something commercially viable. And that's why I started the company to scale things up. The arithmetic intensity of inference workloads that we have today with an ultra aggressive model is very bad. The utilization is very low and that's why people are building massive data centers or even building custom chips, AI inference chips that are better suited for that kind of work. Basically, if you can generate more tokens per second, What this means is that for the same amount of hardware, for the same number of GPUs, you can produce more tokens.
And so the cost per token is going to go down. And that's why we're able to serve our models much more cheaply than what you would get because we make better use of the existing hardware. So now the Mercury models that we have in production are significantly larger. They've been trained on more data. That's going to enable Mercury models to be even smarter. It's going to have much better planning and kind of like reasoning capabilities. And so that's going to enable a lot of agentic use cases that people really care about. They're going to make them really, really fast.
Welcome, humans, to the Neuron AI Podcast. I'm your host, Corey Knowles, and I'm joined, as always, by the man who could turn a GPU benchmark into a bedtime story, Grant Harvey. How's it going today, man?
It's going great.
What is diffusion and how does it differ from traditional models?
Don't put me on the spot to do that right this moment, though. I'd have to think of some mechanics there.
Oh, well, here in just a few, we're going to be joined by Stefano Orman, Stanford University computer science professor and the founder of Inception Labs that created the Mercury Diffusion Large Language Models. But first, Brant's going to share us a little context before we get in there.
Yeah, so image diffusion models work in an entirely different way than the next token predicting GPT models. So we've invited Stefano today because he's taken that same technology and applied it to LLMs. And it has the potential to transform how AI is used in all types of settings from agents to complex enterprise workflows.
Excellent. Well, before we bring him on, I want to take just a quick second to show you Mercury in action because I think seeing it really matters and will keep you really interested. You'll understand why you need to be watching this video. So what you see here on my screen, this is the Inception Lab site. And if you go up top, you can go to Mercury Chat. And down here, I'm just going to grab one of their suggested prompts. And I love this one. Simulate a roundtable discussion between Einstein, Ada Lovelace, and Alan Turing. Now you want to make sure you click this diffusion button because the diffusion button gives you the visual of how cool this is. So watch this. All right. Watch how this works.
Whoa.
And if you go down.
That is so much cooler than the typewriter effect of the AI.
Is that not insane?
Yeah, that's awesome. It's really cool, too, when you do this, like when you build a game in HTML5, like how quickly it can make you something like Pong or any kind of 2D game. It's amazing.
It is. It is.
Well, before we get to this interview, please take a quick second to like and subscribe to the channel so we can keep bringing you the most interesting people in tech and AI.
And with that, welcome to The Neuron. Stefano, it's great to have you.
Thank you. Pleasure to be here. Good to see you again.
Excellent. Well, we're so excited to have you on. As I mentioned before we started, I had we chatted in Vegas, did a short interview. And I've really been looking forward to this because I just I had so many questions when I walked away still that I was like, oh, we've got to get him on. We've got to get him on. So I guess to start, would you mind kind of explaining diffusion in a fairly simple way for viewers who maybe aren't familiar?
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